quantile

UnitsAwareDataArray.quantile(q: ArrayLike, dim: Dims = None, *, method: QuantileMethods = 'linear', keep_attrs: bool | None = None, skipna: bool | None = None, interpolation: QuantileMethods | None = None) → Self

Compute the qth quantile of the data along the specified dimension.

Returns the qth quantiles(s) of the array elements.

Parameters:

  • q (float or array-like of float) – Quantile to compute, which must be between 0 and 1 inclusive.

  • dim (str or Iterable of Hashable, optional) – Dimension(s) over which to apply quantile.

  • method (str, default: "linear") –

    This optional parameter specifies the interpolation method to use when the desired quantile lies between two data points. The options sorted by their R type as summarized in the H&F paper [1] are:

    1. ”inverted_cdf”

    2. ”averaged_inverted_cdf”

    3. ”closest_observation”

    4. ”interpolated_inverted_cdf”

    5. ”hazen”

    6. ”weibull”

    7. ”linear” (default)

    8. ”median_unbiased”

    9. ”normal_unbiased”

    The first three methods are discontiuous. The following discontinuous variations of the default “linear” (7.) option are also available:

    • ”lower”

    • ”higher”

    • ”midpoint”

    • ”nearest”

    See numpy.quantile() or [1] for details. The “method” argument was previously called “interpolation”, renamed in accordance with numpy version 1.22.0.

  • keep_attrs (bool or None, optional) – If True, the dataset’s attributes (attrs) will be copied from the original object to the new one. If False (default), the new object will be returned without attributes.

  • skipna (bool or None, optional) – If True, skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).

Returns:

quantiles – If q is a single quantile, then the result is a scalar. If multiple percentiles are given, first axis of the result corresponds to the quantile and a quantile dimension is added to the return array. The other dimensions are the dimensions that remain after the reduction of the array.

Return type:

DataArray

See also

numpy.nanquantile, numpy.quantile, pandas.Series.quantile, Dataset.quantile

Examples

>>> da = xr.DataArray(
...     data=[[0.7, 4.2, 9.4, 1.5], [6.5, 7.3, 2.6, 1.9]],
...     coords={"x": [7, 9], "y": [1, 1.5, 2, 2.5]},
...     dims=("x", "y"),
... )
>>> da.quantile(0)  # or da.quantile(0, dim=...)
<xarray.DataArray ()> Size: 8B
array(0.7)
Coordinates:
    quantile  float64 8B 0.0
>>> da.quantile(0, dim="x")
<xarray.DataArray (y: 4)> Size: 32B
array([0.7, 4.2, 2.6, 1.5])
Coordinates:
  * y         (y) float64 32B 1.0 1.5 2.0 2.5
    quantile  float64 8B 0.0
>>> da.quantile([0, 0.5, 1])
<xarray.DataArray (quantile: 3)> Size: 24B
array([0.7, 3.4, 9.4])
Coordinates:
  * quantile  (quantile) float64 24B 0.0 0.5 1.0
>>> da.quantile([0, 0.5, 1], dim="x")
<xarray.DataArray (quantile: 3, y: 4)> Size: 96B
array([[0.7 , 4.2 , 2.6 , 1.5 ],
       [3.6 , 5.75, 6.  , 1.7 ],
       [6.5 , 7.3 , 9.4 , 1.9 ]])
Coordinates:
  * y         (y) float64 32B 1.0 1.5 2.0 2.5
  * quantile  (quantile) float64 24B 0.0 0.5 1.0

References